Zobrazeno 1 - 10
of 5 696
pro vyhledávání: '"A Dieng"'
Large language models (LLMs) are increasingly being used in materials science. However, little attention has been given to benchmarking and standardized evaluation for LLM-based materials property prediction, which hinders progress. We present LLM4Ma
Externí odkaz:
http://arxiv.org/abs/2411.00177
Autor:
Asiedu, Mercy, Tomasev, Nenad, Ghate, Chintan, Tiyasirichokchai, Tiya, Dieng, Awa, Akande, Oluwatosin, Siwo, Geoffrey, Adudans, Steve, Aitkins, Sylvanus, Ehiakhamen, Odianosen, Ndombi, Eric, Heller, Katherine
While large language models (LLMs) have shown promise for medical question answering, there is limited work focused on tropical and infectious disease-specific exploration. We build on an opensource tropical and infectious diseases (TRINDs) dataset,
Externí odkaz:
http://arxiv.org/abs/2409.09201
Autor:
Asiedu, Mercy Nyamewaa, Haykel, Iskandar, Dieng, Awa, Kauer, Kerrie, Ahmed, Tousif, Ofori, Florence, Chan, Charisma, Pfohl, Stephen, Rostamzadeh, Negar, Heller, Katherine
Artificial Intelligence (AI) for health has the potential to significantly change and improve healthcare. However in most African countries, identifying culturally and contextually attuned approaches for deploying these solutions is not well understo
Externí odkaz:
http://arxiv.org/abs/2409.12197
Autor:
Kannen, Nithish, Ahmad, Arif, Andreetto, Marco, Prabhakaran, Vinodkumar, Prabhu, Utsav, Dieng, Adji Bousso, Bhattacharyya, Pushpak, Dave, Shachi
Text-to-Image (T2I) models are being increasingly adopted in diverse global communities where they create visual representations of their unique cultures. Current T2I benchmarks primarily focus on faithfulness, aesthetics, and realism of generated im
Externí odkaz:
http://arxiv.org/abs/2407.06863
Autor:
Yang, Yulong, Feng, Bowen, Wang, Keqin, Leonard, Naomi, Dieng, Adji Bousso, Allen-Blanchette, Christine
From pedestrians to Kuramoto oscillators, interactions between agents govern how a multitude of dynamical systems evolve in space and time. Discovering how these agents relate to each other can improve our understanding of the often complex dynamics
Externí odkaz:
http://arxiv.org/abs/2406.14746
Autor:
Li, Kangming, Rubungo, Andre Niyongabo, Lei, Xiangyun, Persaud, Daniel, Choudhary, Kamal, DeCost, Brian, Dieng, Adji Bousso, Hattrick-Simpers, Jason
Scientific machine learning (ML) endeavors to develop generalizable models with broad applicability. However, the assessment of generalizability is often based on heuristics. Here, we demonstrate in the materials science setting that heuristics based
Externí odkaz:
http://arxiv.org/abs/2406.06489
The diffusion model has shown success in generating high-quality and diverse solutions to trajectory optimization problems. However, diffusion models with neural networks inevitably make prediction errors, which leads to constraint violations such as
Externí odkaz:
http://arxiv.org/abs/2406.00990
This paper introduces alternators, a novel family of non-Markovian dynamical models for sequences. An alternator features two neural networks: the observation trajectory network (OTN) and the feature trajectory network (FTN). The OTN and the FTN work
Externí odkaz:
http://arxiv.org/abs/2405.11848
Autor:
Nguyen, Quan, Dieng, Adji Bousso
Experimental design techniques such as active search and Bayesian optimization are widely used in the natural sciences for data collection and discovery. However, existing techniques tend to favor exploitation over exploration of the search space, wh
Externí odkaz:
http://arxiv.org/abs/2405.02449
Autor:
Pfohl, Stephen R., Cole-Lewis, Heather, Sayres, Rory, Neal, Darlene, Asiedu, Mercy, Dieng, Awa, Tomasev, Nenad, Rashid, Qazi Mamunur, Azizi, Shekoofeh, Rostamzadeh, Negar, McCoy, Liam G., Celi, Leo Anthony, Liu, Yun, Schaekermann, Mike, Walton, Alanna, Parrish, Alicia, Nagpal, Chirag, Singh, Preeti, Dewitt, Akeiylah, Mansfield, Philip, Prakash, Sushant, Heller, Katherine, Karthikesalingam, Alan, Semturs, Christopher, Barral, Joelle, Corrado, Greg, Matias, Yossi, Smith-Loud, Jamila, Horn, Ivor, Singhal, Karan
Publikováno v:
Nature Medicine (2024)
Large language models (LLMs) hold promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evaluating equity-related model failures is a critical step toward developin
Externí odkaz:
http://arxiv.org/abs/2403.12025